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Estimating Dynamic Models of Imperfect Competition Author info | Abstract | Publisher info | Download info | Related research | Statistics Jonathan Levin (Stanford University)
Pat Bajari
Lanier Benkard
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We describe a two-step algorithm for estimating dynamic games where the agents are assumed to play a Markov Perfect equilibrium. In the first step, the policy functions and the law of motion for the state variables are estimated. In the second step, the remaining structural parameters are estimated using the optimality conditions for equilibrium. For non-identified models, we describe a bounds approach to the second step estimation. For identified models, the second step estimator is a simple maximum likelihood estimator that is similar to a probit model. We discuss how the approach applies to dynamic discrete choice models and a class of dynamic oligopoly models with lumpy investment policies.
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Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number
627.
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Date of creation: 11 Aug 2004Date of revision:
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Keywords: dynamic games estimation dynamic discrete choice models Other versions of this item:
Find related papers by JEL classification: D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models
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